Computational framework evaluation demonstrates accurate rate coefficient optimization in complex gas-phase reaction networks, suggesting improved robustness over single kinetic models.
Detailed chemical kinetic mechanisms are widely used to model complex gas-phase reactions in atmospheric and combustion chemistry, but are often underconstrained. In those cases, model predictions may remain accurate due to parameter error cancellation within the validation conditions, yet fail outside of that range. We introduce KiMecO, an uncertainty informed, master equation based kinetic mechanism optimizer that constrains the rate coefficients of a submechanism against concentration–time profiles of multiple species across multiple conditions. It leverages machine learning and parallel computation to explore the sensitive master equation parameter space and yield physically consistent correlations among rate coefficients. We show that ensembles of master equation models are generally more robust than single models and enable uncertainty analysis of the optimized rate coefficients.
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Soulié et al. (2026) studied this question.
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